U.S. Department of Transportation
Federal Highway Administration
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Washington, DC 20590
202-366-4000
Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
This report is an archived publication and may contain dated technical, contact, and link information |
|
Publication Number: FHWA-RD-01-166 Date: November 2003 |
This chapter presents a summary of the SPS-1 experimental data and summarizes the level E data in the IMS based on the LTPP data collection guidelines at the time of the SPS-1 experimental review. Appendix A provides a brief discussion and summary of each SPS-1 project, including a review of the construction difficulties and project deviations from the experimental plan.
As stated in chapter 1, the IMS is a very dynamic database that is continually updated and revised as new data are entered and checked for anomalies. Figure 2 is a generalized flow chart showing the movement of data and the data quality checks through LTPP. This flow chart is useful for understanding why some of the key data that have been collected for a specific test section do not appear as Level E data in the LTPP database.
The quality of the data is the most important factor in any type of analysis. From the outset of the LTPP program, data quality has been considered of paramount concern. Procedures for collecting and processing data were defined and modified as necessary to ensure consistency across various reporting contractors, laboratories, equipment operators, or others. Although these procedures formed the foundation of quality control/quality assurance (QC/QA) and data integrity, many more components of a QC/QA plan were necessary to ensure that the data sent to researchers were as error-free as practical.
LTPP has developed and implemented an extensive QC program that classifies each of the data elements into categories depending upon the location of the data in this QC process. Several components comprise the overall QC/QA plan used on the LTPP data as discussed below.
When the QC/QA programs are completed, the regional engineers review the output and resolve any data errors. Often the data entered are legitimate and accurate, but do not pass a QC/QA check. If this occurs, the regional engineer can document that the data have been confirmed using a comments table in the IMS and can manually upgrade the record to Level E.
Figure 2 shows the movement of data elements and quality checks completed on the data prior to release to the public. Only a fraction of the data fields are checked. A value of A is assigned automatically to a record on entry in the database. A value of B indicates the QC process was executed and a Level C check failed. Any record for which correct section information is stored in the database is available after the QC is completed. A record of the QC processing is included with the record. Since the checks are run in sequence A-E, the last successful check is identified on the record as the record status variable. A value of B or C indicates that a necessary data element was not available when the QC was processed and does not necessarily imply that the higher level QC was unsuccessful.
There are numerous reasons why some data may be unavailable from the publicly released IMS database at the time the data were actually collected. Following are some examples:
Therefore, the missing data identified in this report do not necessarily mean that the data were not collected or submitted by the States. There are several places where data may be delayed and not reach Level E. The results in this report are based only upon Level E because it was impossible to know the specific reasons why that data did not pass all of the QC checks. Many of the reasons that prevent data from reaching Level E status are not the result of poor quality or unreliability of the data. The LTPP program is embarking on a systemwide effort to resolve all unavailable data so that future researchers can access them.
All of the data elements included in the SPS-1 experiment were reviewed for their availability and completeness in the LTPP database as listed in table 5. The data elements were divided into three categories for the review process—essential, explanatory, and informational. Each category is defined briefly below.
Although the review of the SPS-1 experiment included all data elements, the detailed review concentrated on those elements that were identified as essential and explanatory. The key data elements that were evaluated and assessed for determining the quality level and completeness for each project were subdivided into the following types of data, and are discussed in this chapter:
Table 5. Summary of SPS-1 data elements and their importance to experimental expectations.
Module ID |
Data Element |
*Data Avail., % |
Data Importance |
||
---|---|---|---|---|---|
Essential |
Explanatory |
Informational |
|||
Automated Weather Station |
Daily Max Temp |
83 |
X |
||
Daily Min Tem |
X |
||||
Daily Mean Temp |
X |
||||
Maximum Avg Monthly Humidity |
X |
||||
Minimum Avg Monthly Humidity |
X |
||||
Monthly Precipitation |
X |
||||
Number of Days with Precipitation |
X |
||||
Number of Days with Intense Precipitation |
X |
||||
Avg Daily Mean Solar Radiation by Month |
X |
||||
Mean Monthly Temp |
X |
||||
Avg Min Monthly Temp |
X |
||||
Avg Min Monthly Temp |
X |
||||
Days >32 ºC |
X |
||||
Days <0 ºC |
X |
||||
Freeze Index |
X |
||||
Number of Freeze-Thaw Cycles |
X |
||||
Mean by Month of Avg Daily Wind Speed |
X |
||||
Climatic |
Maximum Avg Annual Humidity |
89 |
X |
||
Minimum Avg Annual Humidity |
X |
||||
Annual Precipitation |
X |
||||
Number of Days with Intense Precipitation |
X |
||||
Number of Days with Precipitation |
X |
||||
Annual Snowfall |
X |
||||
Number of Days with Snowfall |
X |
||||
Mean Annual Temp |
X |
||||
Avg Max Annual Temp |
X |
||||
Avg Min Annual Temp |
X |
||||
Max Annual Temp |
X |
||||
Min Annual Temp |
X |
||||
Day >32 °C |
X |
||||
Days <0 °C |
X |
||||
Freeze Index |
X |
||||
Annual Number of Freeze-Thaw Cycles |
X |
||||
Mean Wind Speed |
X |
||||
Maintenance |
Crack Sealing |
0 |
X |
||
Patching |
6 |
X |
|||
Asphalt Seal |
6 |
X |
|||
Monitoring |
Deflections |
100 |
X |
||
Temp at Testing |
94 |
X |
|||
Backcalculated Modulus |
– |
X |
|||
Manual Distress |
100 |
X |
|||
PASCO Distress |
50 |
X |
|||
Friction |
38 |
X |
|||
Longitudinal Profile |
100 |
X |
|||
Transverse Profile |
89 |
X |
|||
Construction |
Layer Thickness |
94 |
X |
||
Rod and Level Thickness |
78 |
X |
|||
Asphalt Grade |
72 |
X |
|||
Aggregate Type |
67 |
X |
|||
Specific Gravity of Aggregate |
56 |
X |
|||
Compaction of the Asphalt |
78 |
X |
|||
Laydown Temp |
72 |
X |
|||
In Situ Density of Bound Layers |
33 |
X |
|||
Mix Design Air Voids |
67 |
X |
|||
Mix Design Asphalt Content |
67 |
X |
|||
Design VMA |
67 |
X |
|||
Design Effective Asphalt Content |
89 |
X |
|||
Marshall Stability |
39 |
X |
|||
Marshall Flow |
39 |
X |
|||
Hveem Stability |
11 |
X |
|||
Hveem Cohesiometer |
0 |
X |
|||
Haul Distance |
83 |
X |
|||
Plant Type |
89 |
X |
|||
Paver Type |
89 |
X |
|||
Laydown Width |
83 |
X |
|||
Lift Thickness |
89 |
X |
|||
Subgrade Stabilization |
39 |
X |
|||
Location |
100 |
X |
|||
Functional Class |
100 |
X |
|||
Elevation |
100 |
X |
|||
Cost |
22 |
X |
|||
Drainage Type |
78 |
X |
|||
Shoulder Type |
78 |
X |
|||
Traffic |
Estimated ESALs |
22 |
X |
||
Estimated AADT |
22 |
X |
|||
W4 Tables |
50 |
X |
|||
Monitored AVC |
50 |
X |
|||
Monitored AADT |
17 |
X |
|||
Monitored ESALs |
39 |
X |
|||
[Materials] |
Core Examination |
85 |
X |
||
Bulk Specific Gravity |
67 |
X |
|||
Max Specific Gravity |
65 |
X |
|||
Asphalt Content |
67 |
X |
|||
Moisture Susceptibility |
44 |
X |
|||
Asphalt Resilient Modulus |
0 |
X |
|||
Ash Content of AC |
44 |
X |
|||
Penetration |
67 |
X |
|||
Asphalt Specific Gravity |
67 |
X |
|||
Viscosity |
67 |
X |
|||
Aggregate Specific Gravity |
67 |
X |
|||
Aggregate Gradation |
67 |
X |
|||
Fine Aggregate Particle Shape |
39 |
X |
|||
In Situ Density |
83 |
X |
|||
Layer Thickness |
67 |
X |
|||
Treated Base Type |
17 |
X |
|||
Treated Base Compressive Strength |
0 |
X |
|||
Unbound Base Gradation |
67 |
X |
|||
Unbound Base Classification |
67 |
X |
|||
Unbound Compressive Strength of the Subgrade |
33 |
X |
|||
Unbound Base Permeability |
39 |
X |
|||
Unbound Base Optimum Moisture |
67 |
X |
|||
Unbound Base Max Density |
67 |
X |
|||
Unbound Base Modulus |
17 |
X |
|||
Unbound Base Moisture Content |
50 |
X |
|||
Subgrade Gradation |
72 |
X |
|||
Subgrade Hydrometer Analysis |
78 |
X |
|||
Subgrade Classification |
78 |
X |
|||
Subgrade Permeability |
33 |
X |
|||
Atterberg Limits |
78 |
X |
|||
Subgrade Max Density |
83 |
X |
|||
Subgrade Modulus |
83 |
X |
|||
Subgrade Moisture Content |
72 |
X |
*Data Availability—percentage of SPS-1 required testing for which data generally are available in the database at Level E.
This assessment includes the site identification and location, key equipment installed at the site, the construction report’s availability, and important dates associated with each of the SPS-1 projects. The information for this review was obtained from the site construction report, deviation report, or from the IMS tables entitled EXPERIMENT_SECTION and SPS_ID. All of the site level records for the 18 constructed SPS-1 projects are at Level E. These data records are complete, as noted in the project summary records presented in appendix A. Table 6 includes a summary of the site information and report availability for each of the projects.
Construction and deviation reports were available for review from all of the projects except Michigan, Wisconsin, and Montana. Montana and Wisconsin are new projects, while the Michigan project is 4 years old. The construction report for the Montana project has been drafted, but is awaiting additional construction information before submittal to LTPP and the Wisconsin construction report was submitted to LTPP after the review had been completed.
AWS equipment has been installed at all sites. However, WIM and Automated Vehicle Classification (AVC) equipment has not been installed at five of the project sites: Alabama, Delaware, Louisiana, Oklahoma, and New Mexico (see table 6). This is considered significant to the experiment, especially when trying to validate the more sophisticated mechanistic-empirical design-analysis procedures. Specifically, reliable and site-specific traffic data are considered vital to National Cooperative Highway Research Program (NCHRP) Project 1-37A, development of the 2002 Guide for the Design of New and Rehabilitated Pavement Structures.
Table 6. SPS-1 project site information and report availability.
Project |
Region |
Age, Years |
Equipment Installed |
Report Availability |
|||
---|---|---|---|---|---|---|---|
AWS |
WIM |
AVC |
Construction |
Deviation |
|||
Delaware |
NA |
3.2 |
X |
X |
X |
||
Virginia |
3.7 |
X |
X |
X |
X |
X |
|
Iowa |
NC |
7.0 |
X |
X |
X (5) |
X |
X |
Kansas |
5.8 |
X |
X |
X |
X |
X |
|
Nebraska |
4.1 |
X |
X |
X |
X |
X |
|
Michigan |
4.0 |
X |
X |
X |
X |
||
Ohio |
4.6 |
X |
X (4) |
X |
X |
||
Wisconsin |
1.8 |
X (3) |
X (3) |
X (3) |
X (3) |
X |
|
Alabama |
S |
6.4 |
X |
X |
X |
||
Arkansas |
5.7 |
X |
X |
X |
X |
X |
|
Florida |
3.7 |
X |
X |
X |
X |
X |
|
Louisiana |
2.1 |
X |
X |
X |
|||
New Mexico |
3.7 |
X |
X |
X |
|||
Oklahoma |
2.1 |
X |
X |
X |
|||
Texas |
2.3 |
X |
X (6) |
X (6) |
X |
X |
|
Arizona |
W |
6.0 |
X |
X |
X |
X |
X |
Montana |
0.8 |
X |
X (2) |
X (2) |
X |
||
Nevada |
4.0 |
X (1) |
X |
X |
X |
X |
Notes:
Chapter 3 presented a summary of the construction and specification requirements for each of the SPS-1 projects. Additionally, the Nomination Guidelines (11) and Construction Guidelines (12) for FHWA’s Guidelines for Nomination and Evaluation of Candidate Projects for Experiment SPS-1 Strategic Study of Structural Factors for Flexible Pavements also established specific site selection criteria and key variable construction guidelines. The guidelines presented in both of these reports were developed to control quality and integrity of the SPS-1 experiment results and findings. Therefore, they should be considered in the construction adequacy evaluation and assessment.
One of the main objectives of this study was to identify any confounding factors introduced into the SPS-1 experiment regarding construction deviations or other factors not accounted for in the original experiment design. It is extremely important to evaluate the types of variables that are considered key design factors in the SPS-1 experiment and to determine if any deviation of the design parameters established for the design factorial will adversely affect the experimental expectations.
This section of the report evaluates the design versus the actual construction of key variables identified within the experimental factorial and the above-mentioned experiment guidelines.
The type of subgrade soil is a key factor in the experimental design. Specifically, the SPS-1 experimental design called for half of the projects to be constructed on coarse-grained soils and the other half to be built over fine-grained soils. An additional requirement of the experiment was that all test sections at a site be constructed on the same type of soil (i.e., the same soil classification). Table 7 provides a summary of the subgrade soils and their classification in comparison to the original nomination (refer to table 1). As tabulated, only one of the sites (Texas) is now listed within a different experimental cell because the subgrade soils were found to be different than originally nominated.
Similarly, the subgrade soils on which these projects were built are relatively consistent for each of the core test sections at a site. In fact, there are only two projects where the subgrade classification varies between the different test sections at a project—Kansas and New Mexico. The test sections with the different soil classifications are noted in table 7 and show that 5 of the 18 test sections in Kansas are classified as coarse-grained subgrades, while only 1 of the 12 test sections in New Mexico is classified as coarse-grained. This subgrade variation is considered typical, and it is not believed that this deviation from the experiment requirement will have a detrimental impact in achieving the expectations of the SPS-1 experiment.
One major discrepancy was noted during the review process. All subgrades are classified by the RCO and this classification is entered into the SPS1_LAYER table, as shown in table 7. In some cases, this classification is different from the soil type identified on table TST_L05B. For example, Kansas, Nevada, and Texas have different classifications between tables TST_L05B and SPS1_LAYER. Thus, an additional check should be added to cross-reference the subgrade soil classification between the TST_L05B and SPS1_LAYER tables to ensure that the same data elements are consistent.
The SPS-1 experimental design called for each project to be located in one of four different climates: wet-freeze, wet-no-freeze, dry-freeze, or dry-no-freeze. The main purpose of this factor was to obtain SPS-1 projects in different climates, as well as a geographical distribution across the United States and Canada. Figure 1 provided a summary of the geographical distribution of these projects across the United States and Canada. Table 8 tabulates the average annual rainfall, mean annual air temperatures, and freezing index that have been measured, which define each site’s climatic zone.
Table 7. SPS-1 subgrade classification.
State | Nominated Soil Type | From TST L05B | From SPS1_LAYER table | ||
---|---|---|---|---|---|
Soil Type |
No. Sections | Class, OK? | |||
AL |
Fine |
Silty Clay |
15 |
X |
Silty Clay |
AZ |
Coarse |
Well-Graded Sand with Silt and Gravel |
3 |
X |
Silty Sand |
Silty Sand with Gravel |
7 |
||||
Clayey Sand with Gravel |
1 |
||||
Well-Graded Gravel with Silt and Sand |
5 |
||||
AK |
Coarse |
Clayey Sand |
12 |
X |
Clayey Sand |
DE |
Coarse |
Poorly Graded Sand |
14 |
X |
Silty Sand |
FL |
Coarse |
Silty Sand with Gravel |
9 |
X |
Poorly Graded Sand |
Poorly Graded Sand with Silt and Gravel |
4 |
X |
|||
IA |
Fine |
Clay |
1 |
X |
Sandy Clay |
Clay with Gravel |
8 |
||||
Clay with Sand |
2 |
||||
Lean Clay with Sand |
1 |
||||
Silty Clay |
1 |
||||
KS |
Fine |
Clay with Sand |
7 |
X |
Sandy Silt |
Sandy Clay |
4 |
||||
Silty Clay |
2 |
||||
Sand |
1 |
||||
Silty Sand |
4 |
||||
LA |
Fine |
Clay |
12 |
X |
Silty Clay |
MI |
Fine |
Sandy Clay |
13 |
X |
|
MT |
Coarse |
Poorly Graded Sand with Silt |
12 |
X |
Silty Sand |
NE |
Fine |
Silty Clay |
12 |
X |
Silty Clay Silt |
NV |
Coarse |
Silty Sand |
66 |
X |
Silty |
Clayey Sand |
|||||
NM |
Fine |
Lean Inorganic Clay |
1 |
X |
Clay, Liquid limit > 50 |
Fat Inorganic Clay |
4 |
||||
Lean Clay with Sand |
2 |
||||
Fat Clay with Sand |
1 |
||||
Sandy Lean Clay |
1 |
||||
Sandy Fat Clay |
2 |
||||
Clayey Sand |
1 |
||||
OH |
Fine |
Silty Clay |
13 |
X |
Silty Clay |
OK |
Fine |
Sandy Clay |
13 |
X |
Sandy Clay |
TX |
Coarse |
Sandy Silt |
20 |
NO |
Silty Sand |
VA |
Fine |
Fat Clay with Gravel |
3 |
X |
Silty Clay |
Silty Clay with Sand |
1 |
||||
Gravelly Silty Clay |
1 |
||||
Sandy Silty Clay with Gravel |
5 |
||||
Silt |
1 |
||||
Sandy Silt with Gravel |
2 |
||||
WI |
Coarse |
X |
Silty Sand |
Table 8. Summary of key factor values for the SPS-1 projects.
Climate |
Subgrade Soil |
Project ID |
Type of Subgrade Soil |
Average Annual Rainfall, mm |
Mean Annual Air Temp. °C |
Freeze Index °C-Day |
Age, Years |
AWS, Days |
WIM, Days |
Estimated KESALs, Year |
---|---|---|---|---|---|---|---|---|---|---|
Wet-Freeze |
Fine-Grained |
IA |
Clay |
982 |
10.8 |
235 |
7.0 |
815 |
108 |
130 |
MI |
Sandy Clay |
870 |
8.6 |
283 |
4.0 |
670 |
250 |
? |
||
OH |
Silty Clay |
972 |
10.1 |
207 |
4.6 |
1,600 |
0(1) |
? |
||
VA |
Silty Clay |
1,142 |
14.1 |
38 |
3.7 |
1,299 |
313 |
? |
||
Coarse-Grained |
DE |
Poorly Graded Sand |
1,145 |
13.3 |
58 |
3.2 |
1,200 |
0 |
203 |
|
WI |
Silty Sand |
? |
? |
? |
1.8 |
0(2) |
0 |
? |
||
Wet-No-Freeze |
Fine-Grained |
AL |
Silty Clay |
1,340 |
17.3 |
9 |
6.4 |
1,394 |
0 |
237 |
LA |
Clay |
1,538 |
12.9 |
2 |
2.1 |
300 |
0 |
524 |
||
Coarse-Grained |
AR |
Clayey Sand |
1,224 |
15.6 |
47 |
5.7 |
1,100 |
89 |
170 |
|
FL |
Silty Sand |
1,325 |
23 |
0 |
3.7 |
800 |
342 |
1,463 |
||
Dry-Freeze |
Fine-Grained |
KS |
Clay |
627 |
12.9 |
136 |
5.8 |
1,000 |
232 |
? |
NE |
Silty Clay |
785 |
11 |
228 |
4.1 |
1,024 |
531 |
119 |
||
Coarse-Grained |
MT |
Poorly Graded Sand |
317 |
7.6 |
200 |
0.8 |
370 |
0(3) |
? |
|
NV |
Clayey Sand |
223 |
9.7 |
156 |
4.0 |
0(4) |
338 |
799 |
||
Dry-No-Freeze |
Fine-Grained |
NM |
Clay |
290 |
15.4 |
5 |
3.7 |
1,075 |
0 |
393 |
OK |
Sandy Clay |
869 |
15.9 |
45 |
2.1 |
400 |
0 |
280 |
||
TX |
Sandy Silt |
561 |
23.3 |
1 |
2.3 |
187 |
0 |
10 |
||
Coarse-Grained |
AZ |
Silty Sand |
241 |
18.3 |
1 |
6.0 |
1,480 |
1,588 |
185 |
|
Notes:
|
The general climatic data include actual measurements from at least one nearby weather station for each LTPP site. In addition, a site-specific statistical estimate, based on as many as five nearby weather stations, is available for each project. These estimates are called virtual weather stations. The IMS contains monthly and average annual summary statistics. Daily data for both the virtual weather stations and actual weather stations are kept off-line. General environmental data available in the IMS are derived from weather data originally collected from the NOAA.
AWS equipment is installed at every SPS-1 project site (refer to table 6). The AWS provides site-specific information for the same parameters as the general environmental tables, but these data are available with monthly, daily, or hourly statistics. The number of days from the AWS at each project site is summarized in table 8. An appreciable amount of climatic data has been collected from the AWS.
The SPS-1 project sites include a wide range of freezing index, temperatures, and annual rainfall, as originally planned. Those sites with an average annual rainfall greater than 1,000 mm are classified as wet and those sites with less than 1,000 mm are classified as dry. Similarly, the sites with a freezing index greater than 60 ºC-days would be classified as a freezing climate and those with less than 60 ºC-days would be designated as a no-freeze climate. It should be noted that the values used to determine the specific climatic cell assignment are arbitrary and only used to ensure that the projects cover a diverse range of climates. An annual rainfall of 1,000 mm was used in some of the earlier LTPP studies, while an annual rainfall of 508 mm is used in the latest version of DATAPAVE® for designating the site as wet or dry. A freezing index value of 60 °C-days was used to determine whether the site falls into a no-freeze or freeze cell while a different value is used in DATAPAVE.
Using these definitions, some sites do not appear to be in the correct experimental cells. For example, Iowa, Michigan, and Ohio all have average annual rainfalls less than 1,000 mm, but are in the experimental cells designated as a wet climate. It is expected that the average rainfall at the project sites will increase with time. Similarly, Virginia was originally nominated for a freezing climate but has an average freezing index of 38 °C-days since construction. It is expected that the average freezing index at this site will increase over time.
All sites are in compliance with the appropriate cell requirements based on the NOAA and historical data. As a result, the climate designations have not been changed on the basis of a few years’ worth of data. These relatively small differences in the average rainfall and freezing index are not considered detrimental to achieving the SPS-1 experimental objectives or expectations. The experimental sites still represent a diverse range of climates across the United States and Canada, as originally planned.
The pavement structure data are divided into two elements—layer data and design features. Important general design features such as drainage, lane width, and shoulder type are included in table SPS_GENERAL. All of the key design feature data are available for all of the SPS-1 test sections, and all are at Level E.
The pavement layer data for the SPS-1 test sections are available from two different sources. These two sources include the rod and level measurements (IMS Table SPS1_LAYER) and thicknesses from the cores recovered on-site (IMS Table TST_L05B). Both of these tables were examined to evaluate the thickness measurements and variation of the layer thickness data for each of the structural layers within the SPS-1 cross-sections. The average thickness of each layer is provided in appendix B for all of the projects for which data are available. The TST_L05B table contains records for all layers for 17 of the 18 projects. Layer information on Wisconsin has yet to become available because this project is new and the data have not undergone the QC process.
The SPS1_LAYER table contains all layer data for the 14 SPS-1 projects that are at Level E. The projects from which construction data do not exist are Wisconsin, Michigan, Montana, and Nebraska. The Montana and Wisconsin projects are relatively new; the data have been collected, but have not passed the entire QC process
In general, the average layer thicknesses for each layer were as originally planned within the construction guidelines for the SPS-1 experiment. The one construction element that was not satisfied included the layer thickness deviations from the planned thickness within the experiment. On every test section and project, the variation of the layer thicknesses was greater than the maximum value identified in the construction guidelines (refer to chapter 3). It is believed that the construction guidelines called for a tolerance that was impractical.
Histograms for each layer type and thickness level were prepared to review the distribution of layer thicknesses for all projects. Examples of these histograms are included in figures 3 through 10. Each figure includes the distribution of layer thicknesses as included in table TST_ L05B and from the construction data or table SPS1_LAYER. As shown, the distributions between the different thickness methods are very similar, and the average values from those thickness determination methods are approximately equal. These thickness variations (or histograms) represent typical construction practices, and all data sets are normally distributed (with the possible exception of the thin [102-mm] DGAB layer). This variation of layer thickness, which is greater than required by the construction guidelines, is not believed to be a detriment to the experiment or to prevent the experimental objectives from being met. None of the thickness data sets for the same material overlap (e.g., 102 mm versus 178 mm for the HMA layers).
The pavement cross-section and material types planned for each test section within the core experiment of each project were generally met and adhered to based on the construction guidelines. The only deviation to the planned cross-sections was for the Iowa project, where a DGAB layer was placed beneath the PATB layer on one of the test sections. This is not believed to have a significant effect on the experiment.
Field and laboratory tests were conducted to establish the properties of each material included in the SPS-1 experiment. The material properties and the variation of those properties, both between and within the test sections, are required to evaluate and explain causes of performance differences between the test sections. Many of these properties or material characteristics are those that are currently used in existing pavement design and analysis methods.
The material sampling and testing requirements are documented in the SPS-1 materials sampling and testing guidelines report.(4) This report contains the development of the SPS-1 sampling and testing plans, field material sampling and testing requirements, and laboratory materials testing requirements for each SPS-1 project site. SPS-1 materials sampling and testing plans for the subgrade and base materials are provided in chapter 3. In addition, the testing requirements for each of the materials are designated in appendix A.
Figure 3. Thickness histograms for the thin HMA layer (102 mm) from tables SPS1_LAYER (construction data) and TST_L05B.
Figure 4. Thickness histograms for the thick HMA layer (178 mm) from tables SPS1_LAYER (construction data) and TST_L05B.
Figure 6. Thickness histograms for the thick ATB layer (203 mm) from tables SPS1_LAYER (construction data) and TST_L05B.
Figure 7. Thickness histograms for the PATB layer from tables SPS1_LAYER (construction data) and TST_L05B.
Figure 8.
Thickness histograms for the 102-mm DGAB layer from tables
SPS1_LAYER
(construction data) and TST_L05B.
Figure 10. Thickness histograms for the 305-mm DGAB layer from tables
SPS1_LAYER
(construction data) and TST_L05B.
Tables 9 through 13 summarize the available test data from selected tests by material type for each of the projects while table 14 provides a summary of the overall materials testing completed for the core test sections. As shown, there is still a substantial amount of testing that needs to be completed to fill the experiment, even for those data elements or material properties identified as essential (refer to table 5). If this testing is not completed (at least for the essential data elements), the missing laboratory test results from most of the SPS-1 projects will have a detrimental impact on the experiment for achieving the experimental objectives and expectations.
To evaluate the relative difference in construction or the in-place properties, histograms of different material properties were prepared. Figures 11 through 13 show the variation of air voids in the different HMA and ATB layers. As shown, these variations are substantial enough to cause a significant difference in performance. In fact, some of the air voids are greater than 10 percent, which indicates inadequate compaction or other mixture problems. These differences in air voids need to be considered and accounted for in any evaluation or analysis of the performance data.
The material test data that are available were further reviewed to evaluate other material and construction variations between and within the different cells of the experiment. Figures 14 and 15 show the gradation test results for the percentage passing the number 4 and 200 sieves for the PATB material. As illustrated, there are only a small percentage of the tests where the measured gradation may significantly restrict the layer’s capacity to remove any surface water infiltration quickly. Figures 16 and 17 show other typical examples of the variability in the percentage passing the number 200 sieve for the HMA surface and ATB layers that exists in this SPS-1 experiment.
This variability is typical for the other materials using standard construction practices for each specific material. These test results suggest that the materials used in construction have similar physical properties.
Traffic data provide estimates of annual vehicle counts by vehicle classification and distribution of axle weights by axle type. Annual traffic summary statistics are stored in the IMS traffic module, when available. These data are supposed to be provided for each year after the roadway was opened to the traffic. For the SPS-1 experiment, traffic data are collected at the project site using a combination of permanent and portable equipment by the individual States and Provinces.
The SPS-1 experiment design
calls for continuous WIM monitoring, as permitted by WIM scale operating
divisions. Table TRF_MONITOR_BASIC_INFO was examined to identify the SPS-1 records with WIM, AVC data, and annual ESAL
estimates. The availability of WIM and
AVC was further classified as “at least 1 day” or “continuous.”
Table 9. Summary of materials testing on the subgrade soils.
Project |
Age, years |
Subgrade Soil Testing—Percent Complete |
||||
---|---|---|---|---|---|---|
Gradation |
Atterberg Limits |
Moist.-Den. Relations |
Resilient Modulus |
Permeability |
||
Iowa |
7.0 |
0 |
0 |
0 |
100 |
0 |
Alabama |
6.4 |
100 |
100 |
100 |
100 |
0 |
Arizona |
6.0 |
100 |
100 |
100 |
35 |
0 |
Kansas |
5.8 |
0 |
100 |
100 |
50 |
0 |
Arkansas |
5.7 |
100 |
100 |
100 |
0 |
0 |
Ohio |
4.6 |
0 |
0 |
0 |
0 |
66 |
Nebraska |
4.1 |
100 |
100 |
100 |
80 |
33 |
Michigan |
4.0 |
35 |
35 |
35 |
85 |
0 |
Nevada |
4.0 |
100 |
100 |
100 |
100 |
50 |
Florida |
3.7 |
100 |
100 |
100 |
100 |
100 |
New Mexico |
3.7 |
100 |
100 |
100 |
100 |
100 |
Virginia |
3.7 |
100 |
100 |
100 |
0 |
100 |
Delaware |
3.2 |
0 |
0 |
0 |
100 |
0 |
Texas |
2.3 |
60 |
0 |
0 |
100 |
0 |
Oklahoma |
2.1 |
100 |
35 |
0 |
100 |
0 |
Louisiana |
2.1 |
100 |
60 |
100 |
100 |
0 |
Wisconsin |
1.8 |
0 |
0 |
0 |
0 |
0 |
Montana |
0.8 |
0 |
0 |
0 |
0 |
0 |
Table 10. Summary of materials testing on the unbound aggregate base materials.
Project |
Age, years |
Unbound Aggregate Base Testing—Percent Complete |
||||
Gradation |
Atterberg Limits |
Moist.-Den. Relations |
Resilient Modulus |
Permeability |
||
---|---|---|---|---|---|---|
Iowa |
7.0 |
33 |
0 |
33 |
0 |
0 |
Alabama |
6.4 |
100 |
0 |
67 |
0 |
0 |
Arizona |
6.0 |
100 |
100 |
100 |
35 |
0 |
Kansas |
5.8 |
0 |
0 |
0 |
0 |
0 |
Arkansas |
5.7 |
0 |
0 |
0 |
0 |
0 |
Ohio |
4.6 |
33 |
0 |
0 |
0 |
66 |
Nebraska |
4.1 |
0 |
100 |
100 |
0 |
0 |
Michigan |
4.0 |
66 |
66 |
66 |
0 |
0 |
Nevada |
4.0 |
100 |
0 |
0 |
0 |
100 |
Florida |
3.7 |
66 |
66 |
66 |
0 |
0 |
New Mexico |
3.7 |
0 |
0 |
0 |
0 |
100 |
Virginia |
3.7 |
100 |
100 |
0 |
0 |
0 |
Delaware |
3.2 |
0 |
0 |
0 |
0 |
66 |
Texas |
2.3 |
0 |
0 |
0 |
100 |
0 |
Oklahoma |
2.1 |
0 |
0 |
0 |
0 |
0 |
Louisiana |
2.1 |
0 |
0 |
0 |
33 |
0 |
Wisconsin |
1.8 |
0 |
0 |
0 |
0 |
0 |
Montana |
0.8 |
0 |
0 |
0 |
0 |
0 |
Table 11. Summary of materials testing on the permeable asphalt treated base mixtures.
Project |
Age, years |
Permeable Asphalt Treated Base Testing— Percent Complete |
|
---|---|---|---|
Asphalt Content |
Gradation |
||
Iowa |
7.0 |
100 |
100 |
Alabama |
6.4 |
0 |
0 |
Arizona |
6.0 |
100 |
100 |
Kansas |
5.8 |
0 |
0 |
Arkansas |
5.7 |
0 |
0 |
Ohio |
4.6 |
100 |
100 |
Nebraska |
4.1 |
67 |
100 |
Michigan |
4.0 |
0 |
0 |
Nevada |
4.0 |
0 |
0 |
Florida |
3.7 |
33 |
33 |
New Mexico |
3.7 |
33 |
33 |
Virginia |
3.7 |
100 |
100 |
Delaware |
3.2 |
66 |
66 |
Texas |
2.3 |
0 |
0 |
Oklahoma |
2.1 |
100 |
100 |
Louisiana |
2.1 |
0 |
0 |
Wisconsin |
1.8 |
0 |
0 |
Montana |
0.8 |
0 |
0 |
Table 12. Summary of materials testing on the asphalt treated base mixtures.
Project |
Age, years |
HMA Testing—Percent Complete |
|||||
---|---|---|---|---|---|---|---|
Core Exam. |
Spec. Grav. Bulk/Rice |
Asphalt Content |
Moisture Suscep. |
Gradation |
AC Viscosity |
||
Iowa |
7.0 |
67 |
0/0 |
100 |
0 |
100 |
50 |
Alabama |
6.4 |
64 |
56/0 |
0 |
0 |
0 |
0 |
Arizona |
6.0 |
100 |
100/100 |
100 |
100 |
100 |
100 |
Kansas |
5.8 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Arkansas |
5.7 |
100 |
0/0 |
0 |
0 |
0 |
0 |
Ohio |
4.6 |
0 |
0/33 |
33 |
0 |
33 |
17 |
Nebraska |
4.1 |
100 |
33/100 |
100 |
0 |
33 |
35 |
Michigan |
4.0 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Nevada |
4.0 |
75 |
5/0 |
0 |
0 |
0 |
0 |
Florida |
3.7 |
100 |
100/100 |
100 |
100 |
100 |
100 |
New Mexico |
3.7 |
100 |
100/100 |
100 |
100 |
100 |
100 |
Virginia |
3.7 |
100 |
66/100 |
100 |
100 |
100 |
50 |
Delaware |
3.2 |
56 |
33/66 |
66 |
0 |
66 |
0 |
Texas |
2.3 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Oklahoma |
2.1 |
100 |
100/100 |
100 |
100 |
100 |
100 |
Louisiana |
2.1 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Wisconsin |
1.8 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Montana |
0.8 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Note: LATB indirect tensile resilient modulus and strength tests are missing for all of the projects.
Table 13. Summary of materials testing on the HMA mixtures.
Project |
Age, years |
HMA Testing—Percent Complete |
|||||
---|---|---|---|---|---|---|---|
Core Exam. |
Spec. Grav. Bulk/Rice |
Asphalt Content |
Moisture Suscep. |
Gradation |
AC Viscosity |
||
Iowa |
7.0 |
0 |
0/67 |
0 |
0 |
0 |
0 |
Alabama |
6.4 |
76 |
82/0 |
0 |
0 |
0 |
0 |
Arizona |
6.0 |
100 |
100/100 |
100 |
0 |
100 |
75 |
Kansas |
5.8 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Arkansas |
5.7 |
100 |
0/0 |
0 |
0 |
0 |
0 |
Ohio |
4.6 |
86 |
35/100 |
100 |
0 |
100 |
75 |
Nebraska |
4.1 |
50 |
30/100 |
33 |
0 |
33 |
18 |
Michigan |
4.0 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Nevada |
4.0 |
100 |
50/0 |
0 |
0 |
0 |
0 |
Florida |
3.7 |
94 |
100/100 |
100 |
100 |
100 |
100 |
New Mexico |
3.7 |
100 |
100/100 |
100 |
100 |
100 |
100 |
Virginia |
3.7 |
100 |
75 |
33 |
33 |
33 |
18 |
Delaware |
3.2 |
81 |
86/0 |
100 |
0 |
100 |
100 |
Texas |
2.3 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Oklahoma |
2.1 |
100 |
100/100 |
100 |
100 |
100 |
100 |
Louisiana |
2.1 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Wisconsin |
1.8 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Montana |
0.8 |
0 |
0/0 |
0 |
0 |
0 |
0 |
Note: HMA indirect tensile resilient modulus, strength, and creep compliance tests are missing for all of the projects.
Table 14. Summary of materials testing completed by material type for the core test sections, percent complete.
Climate |
Subgrade Soil Classification |
Project ID | Shoulder |
Material |
||||
---|---|---|---|---|---|---|---|---|
HMA Surface |
Dense Graded Aggregate Base |
Asphalt Treated Base |
Permeable Asphalt Treated Base |
Subgrade |
||||
Wet-Freeze |
Fine-Grained |
Iowa |
HMA |
6 |
11 |
56 |
100 |
14 |
Michigan |
HMA |
0 |
45 |
0 |
0 |
35 |
||
Ohio |
HMA |
82 |
17 |
19 |
100 |
9 |
||
Coarse-Grained |
Delaware |
HMA |
75 |
12 |
25 |
67 |
14 |
|
Virginia |
HMA |
43 |
50 |
76 |
100 |
71 |
||
Wisconsin |
HMA |
0 |
0 |
0 |
0 |
0 |
||
Wet-No-Freeze |
Fine-Grained |
Alabama |
HMA |
10 |
40 |
8 |
0 |
86 |
Louisiana |
HMA |
0 |
6 |
0 |
0 |
71 |
||
Coarse-Grained |
Arkansas |
HMA |
9 |
0 |
9 |
0 |
62 |
|
Florida |
HMA |
86 |
45 |
72 |
33 |
86 |
||
Dry-Freeze |
Fine-Grained |
Kansas |
? |
0 |
0 |
0 |
0 |
48 |
Nebraska |
HMA |
36 |
33 |
57 |
84 |
76 |
||
Coarse-Grained |
Montana |
HMA |
0 |
0 |
0 |
0 |
0 |
|
Nevada |
HMA |
14 |
50 |
6 |
0 |
91 |
||
Dry-No-Freeze |
Fine-Grained |
New Mexico |
HMA |
86 |
33 |
90 |
33 |
100 |
Oklahoma |
HMA |
78 |
0 |
90 |
100 |
48 |
||
Texas |
HMA |
0 |
0 |
0 |
0 |
57 |
||
Coarse-Grained |
Arizona |
None |
73 |
50 |
73 |
100 |
76 |
Note: The materials testing for the Wisconsin project is underway, but was not at Level E in the January 2000 release.
Figure 11. Histogram of air voids measured on the HMA surface layer.
Figure 12. Histogram of air voids measured on the HMA binder layer.
Figure 13. Histogram of air voids measured on the ATB layer.
Figure 14. Histogram of the material passing the number 4 sieve, PATB layer.
Figure 15. Histogram of material passing the number 200 sieve, PATB layer.
Figure
16. Histogram of material passing the
number 200 sieve, HMA surface layer.
Figure 17. Histogram of material passing the number 200 sieve, ATB layer.
Continuous AVC and WIM monitoring were defined for two different conditions. In the past, LTPP has defined continuous AVC monitoring as over 300 AVC monitoring days in a given year and continuous WIM monitoring as over 210 WIM monitoring days in a given year. However, based on variability measurements and the minimum number of sampling days being recommended from NCHRP Project 1-37A for sampling truck traffic, continuous AVC and WIM monitoring are defined as over 45 monitoring days in a given season.
Table 8 provided a summary of the number of continuous WIM days available at each of the project sites. Table 6 also identified those sites where WIM and AVC equipment had been installed. As tabulated, over 50 percent of the SPS-1 projects do not have any WIM or AVC data at Level E in the IMS. As previously stated, this is considered a significant detriment to achieving the experimental objectives and expectations. On the positive side, WIM and AVC equipment have been installed at the Iowa, Ohio, Montana, Texas, and Wisconsin sites, but the data collected are not at Level E. Table 15 shows that the greatest amount of Level E traffic data, in general, are available for the oldest projects, as expected.
In the original SPS-1 experimental design, traffic was incorporated as a covariant in the experimental design. A traffic level of at least 100,000 ESALs per year was required for each of the projects. The actual ESALs per year at each site are shown in table 8 (note that initial estimated ESALs are unavailable for six sites). The traffic level requirement was met for all of the sites except the Texas project, which is located along a rural route with little truck traffic. The project with the highest annual ESALs is the Florida project (1,463,000 per year).
Table 15. Summary of climatic and traffic data for the SPS-1 project sites.
Project |
Age, years |
Climate Data |
Traffic Data |
|||
---|---|---|---|---|---|---|
Equipment installed |
Number of AWS days |
Equipment installed |
Number of WIM days |
Number of AVC days |
||
Iowa |
7.0 |
815 |
108 |
– |
||
Alabama |
6.4 |
1,394 |
– |
– |
– |
|
Arizona |
6.0 |
1,480 |
1,588 |
1,544 |
||
Kansas |
5.8 |
1,000 |
232 |
18 |
||
Arkansas |
5.7 |
1,100 |
89 |
89 |
||
Ohio |
4.6 |
1,600 |
– |
– |
||
Nebraska |
4.1 |
1,024 |
531 |
581 |
||
Michigan |
4.0 |
670 |
250 |
– |
||
Nevada |
4.0 |
– |
338 |
299 |
||
Florida |
3.7 |
800 |
342 |
220 |
||
New Mexico |
3.7 |
1,075 |
– |
– |
– |
|
Virginia |
3.7 |
1,299 |
313 |
312 |
||
Delaware |
3.2 |
1,200 |
– |
– |
– |
|
Texas |
2.3 |
187 |
– |
– |
||
Oklahoma |
2.1 |
400 |
– |
– |
– |
|
Louisiana |
2.1 |
300 |
– |
– |
– |
|
Wisconsin |
1.8 |
– |
– |
– |
||
Montana |
0.8 |
370 |
– |
– |
The range of traffic loadings between the sites will need to be fully considered in any comparative analysis of these data. More importantly, the missing traffic data will severely restrict the use of the SPS-1 experiment data for validating mechanistic-empirical design and analysis methods.
Several types of monitoring data are included in the LTPP IMS. These monitoring data include distresses (from both manual and automated or PASCO surveys), longitudinal profiles, transverse profiles, deflection, and friction. Chapter 3 of this report reviewed the required monitoring frequency for each of the data elements for the SPS-1 experiment. In general, these minimum requirements are being met for the long-term monitoring frequency, but have not been met for the initial data collection requirements. The number of measurements for each of the test sections within each project was tabulated and discussed in appendix A.
Table 16 provides a summary of the minimum number of distress and other performance indicator measurements made at each of the SPS-1 project sites. As tabulated, very few friction measurements have been performed on these projects, while there have been numerous deflection and manual distress surveys. At least one survey for each of the monitoring data elements has been made at each site, with the exception of the friction surveys and transverse profile measurements at selected sites.
Table 16. Summary of the minimum number of distress and other performance indicator measurements made at each project site.
Project |
Region |
Age, Years |
Deflection Surveys |
Distress |
Transverse Profiles |
Longitudinal Profiles |
Friction Surveys |
|
---|---|---|---|---|---|---|---|---|
Manual |
Pasco |
|||||||
Delaware |
North Atlantic |
3.2 |
1 |
3 |
0 |
2 |
7 |
0 |
Virginia |
3.7 |
2 |
3 |
0 |
2 |
7 |
1 |
|
Iowa |
North Central |
7.0 |
3 |
1 |
2 |
4 |
6 |
5 |
Kansas |
5.8 |
6 |
4 |
2 |
7 |
6 |
6 |
|
Nebraska |
4.1 |
2 |
2 |
1 |
3 |
4 |
0(1) |
|
Michigan |
4.0 |
2 |
4 |
1 |
2 |
3 |
1 |
|
Ohio |
4.6 |
5 |
3 |
1 |
3 |
4 |
0 |
|
Wisconsin |
1.8 |
1 |
1 |
0 |
0(2) |
3 |
0 |
|
Alabama |
South |
6.4 |
5 |
3 |
2 |
2 |
4 |
2 |
Arkansas |
5.7 |
5 |
1 |
1 |
1 |
2 |
0 |
|
Florida |
3.7 |
1 |
1 |
0 |
0 |
4 |
0 |
|
Louisiana |
2.1 |
3 |
2 |
0 |
1 |
1 |
0 |
|
New Mexico |
3.7 |
1 |
2 |
0 |
2 |
1 |
0 |
|
Oklahoma |
2.1 |
2 |
3 |
0 |
0 |
1 |
0 |
|
Texas |
2.3 |
1 |
3 |
0 |
2 |
2 |
0 |
|
Arizona |
West |
6.0 |
7 |
3 |
1 |
4 |
5 |
0 |
Montana |
0.8 |
3 |
2 |
0 |
0(3) |
2 |
0 |
|
Nevada |
4.0 |
6 |
3 |
1 |
3 |
3 |
0 |
|
Note:
|
Transverse profiles are not yet measured at the Oklahoma and Florida sites. Transverse profiles were measured for the Montana and Wisconsin projects, but those data are not yet at Level E and are unavailable in the IMS. The Wisconsin and Montana sites are relatively new, whereas the Oklahoma and Florida sites are over 2 years of age.
Longitudinal profiles have been completed at least once for all SPS-1 projects. However, the first longitudinal profile measured on 9 of the 18 projects was more than 1 year after construction. The discrepancy for the initial measurements may be related to the definition of the construction date or other scheduling difficulties as identified in chapter 3. In some cases, the construction date was defined as completion of pavement placement rather than acceptance by the State agency. Those projects that were more than 1 year in age before the first longitudinal profile measurement was taken include Alabama, Arkansas, Florida, Iowa, Michigan, Nevada, New Mexico, Ohio, and Virginia.
Table 17 summarizes the age, in years, between each set of measurements for each performance indicator. Most of the monitored data have been measured more frequently than required by the guidelines referenced in chapter 3.
Table 17. Summary of the average time interval between the different performance indicator surveys.
Project |
Age, years |
Long. Profiles |
Transverse Profiles |
Distress |
Deflection Surveys |
|
---|---|---|---|---|---|---|
Manual |
Pasco |
|||||
Iowa |
7.0 |
1.2 |
1.8 |
7.0 |
3.5 |
2.3 |
Alabama |
6.4 |
1.6 |
3.2 |
2.1 |
3.2 |
1.3 |
Arizona |
6.0 |
1.2 |
1.5 |
2.0 |
6.0 |
0.9 |
Kansas |
5.8 |
1.0 |
0.8 |
1.5 |
2.9 |
1.0 |
Arkansas |
5.7 |
2.9 |
5.7 |
5.7 |
5.7 |
1.3 |
Ohio |
4.6 |
1.2 |
1.5 |
1.5 |
4.6 |
0.9 |
Nebraska |
4.1 |
1.0 |
1.4 |
2.1 |
4.1 |
2.1 |
Michigan |
4.0 |
1.3 |
2.0 |
1.0 |
4.0 |
2.0 |
Nevada |
4.0 |
1.3 |
1.3 |
1.3 |
4.0 |
0.7 |
Florida |
3.7 |
0.9 |
– |
3.7 |
– |
3.7 |
New Mexico |
3.7 |
3.7 |
1.9 |
1.9 |
– |
3.7 |
Virginia |
3.7 |
0.5 |
1.9 |
1.2 |
– |
1.9 |
Delaware |
3.2 |
0.5 |
1.6 |
1.1 |
– |
3.2 |
Texas |
2.3 |
1.2 |
1.2 |
0.8 |
– |
2.3 |
Oklahoma |
2.1 |
2.1 |
– |
0.7 |
– |
1.1 |
Louisiana |
2.1 |
2.1 |
2.1 |
1.1 |
– |
0.7 |
Wisconsin |
1.8 |
0.6 |
– |
1.8 |
– |
1.8 |
Montana |
0.8 |
0.4 |
– |
0.4 |
– |
0.3 |
The data availability assessment of these tables is provided in table 18. All records in these tables are at Level E.
Table 18. Summary of Level E dynamic load response data for the Ohio SPS-1 project.
Data Table Name |
Total Records (All at E) |
Records for Each Section |
|||
---|---|---|---|---|---|
0102 |
0104 |
0108 |
0110 |
||
DLR_LVDT_CONFIG_AC |
131 |
16 |
34 |
40 |
41 |
DLR_LVDT_TRACE_SUM_AC |
348 |
74 |
96 |
98 |
80 |
DLR_MASTER_AC |
23 |
4 |
7 |
6 |
6 |
DLR_PRESSURE_CONFIG_AC |
52 |
8 |
16 |
14 |
14 |
DLR_PRESSURE_TRACE_SUM_AC |
335 |
71 |
121 |
79 |
64 |
DLR_STRAIN_CONFIG_AC |
571 |
72 |
192 |
147 |
160 |
DLR_STRAIN_TRACE_SUM_AC |
304 |
18 |
86 |
88 |
112 |
dlr_test_matrix |
350 |
48 |
111 |
92 |
99 |
DLR_TRUCK_GEOMETRY |
1 |
1 Truck ID/Type |
Table 19 presents an overall summary of the SPS-1 projects, identifying the project deviations, construction difficulties, and overall data completeness. These factors have been aggregated into an “adequacy code,” which consists of a numerical scale from 0 to 5 and provides an overall rating of the project and test sections for fulfilling the original experimental objectives and expectations. A definition of this numerical scale is given below.
0 | = | The project will be unable to meet the experimental objectives and expectations or the project has been recently constructed and has only limited data at this time. |
---|---|---|
1 | = | The project has major limitations in the data. There are significant data deficiencies/missing data that will have a significant detrimental impact on meeting the experimental objectives and expectations. |
2 | = | The project has missing data that will have an impact on the reliability of the results for achieving the experimental objectives and expectations. |
3 | = | The project has some missing data and deficiencies. However, assumptions combined with the existing data can be used to meet the experimental objectives and expectations. |
4 | = | The project has minor limitations, missing data, or data deficiencies that will have little impact on meeting the experimental objectives and expectations. |
5 | = | The project has adequate data to meet the experimental objectives and expectations. |
Relatively few project deviations and problems were encountered during the construction of these projects. Of those difficulties and deviations noted, none are considered fatal to the overall expectations of the projects included in this experiment. However, some data elements at specific project sites will have a negative effect on accomplishing the experimental objectives if they are not collected in the future. Primarily, these include traffic data and some of the materials/layer properties. The omission of these data elements is reflected in the overall adequacy code for each project.
As listed in table 19, two projects have an adequacy code of 0. The Montana and Wisconsin projects are newly constructed and have little data in the database at this time. It is expected that the adequacy of these two projects will increase as more data become available and are entered into the IMS.
Three projects have an adequacy code of 2: Alabama, Louisiana, and Oklahoma. None of these projects have WIM equipment installed at the site; all have substantial materials test data that are missing; and most have missing or infrequent monitoring data.
Six of the projects were assigned an adequacy code of 3 for a variety of reasons. These projects include Arkansas, Delaware, Florida, Michigan, New Mexico, and Texas. The traffic data and a substantial amount of materials test data are unavailable for most of these projects. The adequacy code of these projects will increase as the data reach Level E.
Table 19. Summary of the overall construction difficulties and deviations, and the adequacy code for the projects included in the SPS-1 experiment.
Project |
Construction Difficulties and Deviations |
Adequacy Code |
---|---|---|
Alabama |
|
2 |
Arizona |
|
5 |
Arkansas |
|
3 |
Delaware |
|
3 |
Florida |
|
3 |
Iowa |
|
4 |
Kansas |
|
4 |
Louisiana |
|
2 |
Michigan |
|
3 |
Montana |
|
0 |
Nebraska |
|
4 |
Nevada |
|
4 |
New Mexico |
|
3 |
Ohio |
|
4 |
Oklahoma |
|
2 |
Texas |
|
3 |
Virginia |
|
4 |
Wisconsin |
|
0 |
The Michigan project has an adequacy code of 3 rather than 4 because four of the test sections were taken out of service without measurements for distress, longitudinal profile, international roughness index (IRI), or transverse profile (rut depth). These deficiencies should have minimal impact on the SPS-1 experiment because the project is in the wet-freeze climate and fine-grained subgrade soil site factorial cell of the experiment (refer to table 3), and there are three other projects within this cell that will provide sufficient data for analysis purposes. In addition, the value of this project will increase after the test results reach a Level E status. All other projects were assigned an adequacy code of 4 or 5.